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2018

Some Theoretical Properties of GANs

Biau, G., Cadre, B., Sangnier, M. et al.

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Understand

Generative Adversarial Networks (GANs) are a class of generative algorithms that have been shown to produce state-of-the art samples, especially in the domain of image creation.

The fundamental principle of GANs is to approximate the unknown distribution of a given data set by optimizing an objective function through an adversarial game between a family of generators and a family of discriminators.
In this paper, we offer a better theoretical understanding of GANs by analyzing some of their mathematical and statistical properties.
We study the deep connection between the adversarial principle underlying GANs and the Jensen-Shannon divergence, together with some optimality characteristics of the problem.

Some Theoretical Properties of GANs · Around

Built on

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Universal smoothing factor selection in density estimation: Theory and practice
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A new metric for probability distributions
D.M. Endres and J.E. Schindelin · 2003
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Generative adversarial nets
I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and J. Bengio · 2014
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Training generative neural networks via maximum mean discrepancy optimization
G.K. Dziugaite, D.M. Roy, and Z. Ghahramani · 2015
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